Consumption-Based Forecasting and Planning

Consumption-Based Forecasting and Planning

by Charles W. Chase
Released August 2021
Publisher(s): Wiley
ISBN: 9781119809869

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Book description

Discover a new, demand-centric framework for forecasting and demand planning

In Consumption-Based Forecasting and Planning, thought leader and forecasting expert  Charles W. Chase delivers a practical and novel approach to retail and consumer goods companies demand planning process. The author demonstrates why a demand-centric approach relying on point-of-sale and syndicated scanner data is necessary for success in the new digital economy.

The book showcases short- and mid-term demand sensing and focuses on disruptions to the marketplace caused by the digital economy and COVID-19. You’ll also learn:

  • How to improve demand forecasting and planning accuracy, reduce inventory costs, and minimize waste and stock-outs
  • What is driving shifting consumer demand patterns, including factors like price, promotions, in-store merchandising, and unplanned and unexpected events
  • How to apply analytics and machine learning to your forecasting challenges using proven approaches and tactics described throughout the book via several case studies.   

Perfect for executives, directors, and managers at retailers, consumer products companies, and other manufacturers, Consumption-Based Forecasting and Planning will also earn a place in the libraries of sales, marketing, supply chain, and finance professionals seeking to sharpen their understanding of how to predict future consumer demand.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Foreword
  5. Preface
    1. WHY IS THIS IMPORTANT?
    2. TRACKING SHIFTING CONSUMER DEMAND PATTERNS
  6. Acknowledgments
  7. About the Author
  8. Chapter 1: The Digital Economy and Unexpected Disruptions
    1. DISRUPTIONS DRIVING COMPLEX CONSUMER DYNAMICS
    2. IMPACT OF THE DIGITAL ECONOMY
    3. WHAT DOES ALL THIS MEAN?
    4. SHIFTING TO A CONSUMER-CENTRIC APPROACH
    5. THE ANALYTICS GAP
    6. WHY PREDICTIVE AND ANTICIPATORY ANALYTICS?
    7. DIFFERENCE BETWEEN PREDICTIVE AND ANTICIPATORY ANALYTICS
    8. THE DATA GAP
    9. THE IMPACT OF THE COVID-19 CRISIS ON DEMAND PLANNING
    10. CLOSING THOUGHTS
    11. NOTES
  9. Chapter 2: A Wake-up Call for Demand Management
    1. DEMAND UNCERTAINTY IS DRIVING CHANGE
    2. CHALLENGES CREATED BY DEMAND UNCERTAINTY
    3. ONGOING “BULLWHIP” EFFECT
    4. WHEN WILL WE LEARN FROM OUR PAST MISTAKES?
    5. WHY ARE COMPANIES STILL CLEANSING HISTORICAL DEMAND?
    6. CONSUMER GOODS COMPANY CASE STUDY
    7. PRIMARY OBSTACLES TO ACHIEVING PLANNING GOALS
    8. WHY DO COMPANIES CONTINUE TO DISMISS THE VALUE OF DEMAND MANAGEMENT?
    9. SIX STEPS TO PREDICTING SHIFTING CONSUMER DEMAND PATTERNS
    10. CLOSING THOUGHTS
    11. NOTES
  10. Chapter 3: Why Data and Analytics Are Important
    1. ANALYTICS MATURITY
    2. COLLECTING AND STORING CONSUMER DATA
    3. BUILDING TRUST IN THE DATA
    4. AI/MACHINE LEARNING CREATES TRUST CHALLENGES
    5. PURSUIT OF EXPLAINABILITY
    6. HOW MUCH DATA SHOULD BE USED?
    7. WHAT ARE USERS LOOKING TO GAIN?
    8. CLOSING THOUGHTS
    9. NOTES
  11. Chapter 4: Consumption-Based Forecasting and Planning
    1. A CHANGE OF MINDSET IS REQUIRED
    2. WHY CONSUMPTION-BASED FORECASTING AND PLANNING?
    3. WHAT IS CONSUMPTION-BASED FORECASTING AND PLANNING?
    4. CONSUMPTION-BASED FORECASTING AND PLANNING CASE STUDY
    5. CONSUMPTION-BASED FORECASTING AND PLANNING SIX-STEP PROCESS
    6. UNDERSTANDING THE RELATIONSHIP BETWEEN DEMAND AND SUPPLY
    7. WHY MOVE DEMAND PLANNING DOWNSTREAM CLOSER TO THE CONSUMER?
    8. THE INTEGRATED BUSINESS PLANNING CONNECTION
    9. DEMAND MANAGEMENT CHAMPION
    10. CLOSING THOUGHTS
    11. NOTES
  12. Chapter 5: AI/Machine Learning Is Disrupting Demand Forecasting
    1. STRAIGHT TALK ABOUT FORECASTING AND MACHINE LEARNING
    2. WHAT IS THE DIFFERENCE BETWEEN EXPERT SYSTEMS AND MACHINE LEARNING?
    3. DO MACHINE LEARNING ALGORITHMS OUTPERFORM TRADITIONAL FORECASTING METHODS?
    4. M4 COMPETITION
    5. M5 COMPETITION
    6. BASIC KNOWLEDGE REGARDING NEURAL NETWORKS
    7. WHY COMBINE ML MODELS?
    8. CHALLENGES USING MACHINE LEARNING MODELS
    9. DATA CHALLENGES AND CONSIDERATIONS
    10. CASE STUDY 1
    11. CASE STUDY 2: USING ADVANCED ANALYTICS TO ADAPT TO CHANGING CONSUMER DEMAND PATTERNS
    12. CLOSING THOUGHTS
    13. NOTES
  13. Chapter 6: Intelligent Automation Is Disrupting Demand Planning
    1. WHAT IS “INTELLIGENT AUTOMATION”?
    2. HOW CAN INTELLIGENT AUTOMATION ENHANCE EXISTING PROCESSES?
    3. WHAT IS FORECAST VALUE ADD?
    4. CASE STUDY: USING INTELLIGENT AUTOMATION TO IMPROVE DEMAND PLANNERS‘ FVA
    5. CLOSING THOUGHTS
    6. NOTES
  14. Chapter 7: The Future Is Cloud Analytics and Analytics at the Edge
    1. WHY CLOUD ANALYTICS?
    2. WHAT ARE THE DIFFERENCES BETWEEN CONTAINERS AND VIRTUAL MACHINES?
    3. WHY CLOUD ANALYTICS?
    4. PREDICTIVE ANALYTICS ARE CREATING IT DISRUPTIONS
    5. DATA IS INFLUENCING SOFTWARE DEVELOPMENT
    6. WHY CLOUD-NATIVE SOLUTIONS?
    7. WHY DOES ALL THIS MATTER?
    8. CLOUD-NATIVE FORECASTING AND PLANNING SOLUTIONS
    9. WHY MOVE TO A CLOUD-NATIVE DEMAND PLANNING PLATFORM?
    10. WHY “ANALYTICS AT THE EDGE”?
    11. EDGE ANALYTICS BENEFITS
    12. EDGE ANALYTICS LIMITATIONS
    13. FORECASTING AT THE EDGE
    14. CLOUD ANALYTICS VERSUS EDGE ANALYTICS
    15. CLOSING THOUGHTS
    16. NOTES
  15. Index
  16. End User License Agreement

Product information

  • Title: Consumption-Based Forecasting and Planning
  • Author(s): Charles W. Chase
  • Release date: August 2021
  • Publisher(s): Wiley
  • ISBN: 9781119809869